Performance of Disease Activity Indices Used in Axial Spondyloarthritis in Real-World Clinical Settings
Bibliographic record
Abstract
OBJECTIVE: Monitoring axial spondyloarthritis (axSpA) disease activity using validated indices (eg, Bath Ankylosing Spondylitis Disease Activity Index [BASDAI], Axial Spondyloarthritis Disease Activity Score [ASDAS]) is widely recommended but rarely followed in practice. The reasons, although varied, may be found in the scarcity of studies comparing the performance of these indices in daily practice. Here, we compare the performance of disease activity indices in clinical practice. METHODS: This was an observational cross-sectional study involving 330 patients. The BASDAI, ASDAS, Bath Ankylosing Spondylitis Functional Index (BASFI), and Assessment of SpondyloArthritis international Society Health Index (ASAS HI) indices were included. Their correlations, degree of concordance, and discriminating capacity for different levels of disease activity and impact were compared using the appropriate statistics. RESULTS: ≥ 0.73). Concordance between instruments was substantial, both with regard to the different activity thresholds and the different disease impact categories (κ ≥ 0.61). BASDAI cutoffs of 3.95 (area under the receiver-operating characteristic curve [AUC] 0.90) and 5.85 (AUC 0.90) accurately identified the ASDAS high and very high activity categories, respectively. An ASDAS ≥ 2.1 (AUC 0.87) and a BASDAI ≥ 3 (AUC 0.92) accurately discriminated the ASAS HI high impact category. Regardless of systemic therapy use, there was substantial agreement between BASDAI remission (≤ 2) and ASDAS inactive disease (< 1.3). CONCLUSION: The metrological performance of standard disease activity indices in axSpA were similar. The BASDAI values that identify the ASDAS categories are novel. We suggest using these indices interchangeably in routine clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".